Moving target indication ("MTI") techniques can automatically detect motion of a moving target, based upon a sequence of images acquired by a sensor. In military applications, MTI techniques are used to detect motion of a target moving either along the ground or through the air; the images may be acquired by either a ground or air based sensor. Using imagery from electro-optical sensors such as infrared or TV cameras, the first step in MTI is the measurement of motion in image sequences. The measurement of motion between two images is represented by a two dimensional vector field in the image plane, each vector representing the motion at a specific pixel location. Such a vector field is known as an "optical flow field" and any single vector in the field is known as an "optical flow vector". Accordingly, the optical flow field includes an optical flow vector (u,v) for each pixel. The optical flow vector (u,v) for a pixel of a first image indicates the pixel's direction of movement relative to a second image during the .DELTA.t time period between the first and second images.
The accuracy of an MTI technique can be improved by improving the accuracy of the optical flow field determined between successive images acquired by the sensor. By accurately determining such an optical flow field, target movement between the successive images can be accurately determined. The computation and use of optical flow is an active research topic in the field of computer vision/digital image processing. Nevertheless, previous techniques typically fail to address realistic situations where computation of optical flow uses noisy imagery. Typical previous optical flow computation techniques are based upon fairly idealized assumptions. Such techniques are noise sensitive and frequently impractical in real applications.
For example, differential techniques for computing dense optical flow fields are based on certain assumptions about the spatio-temporal variations of image intensity. The most common assumption (known as the intensity constancy assumption) is that the total temporal derivative of the image intensity is zero. Accordingly, the intensity constancy assumption requires that the image intensity corresponding to a physical surface patch remain unchanged over time.
Despite its widespread use, intensity constancy is not a realistic assumption in most practical situations. The intensity constancy assumption is not satisfied when parts of a surface are obscured or revealed in successive image frames, or when the surface or illumination source moves so that illumination is incident on the surface from different angles in successive frames. Such situations cause the surface shading to vary, thereby violating the intensity constancy assumption.
Even if the radiation received as sensor input satisfies the intensity constancy assumption, the sensor output of image intensity is corrupted by sensor noise. Random temporal variation of this noise component results in random fluctuations in image intensity values over time. Intensity constancy is an unrealistic assumption for optical flow computation where such noise contributions are a significant part of the measured image intensity values. This issue is important in practical situations, particularly in several defense applications, where the expendable nature of the sensor precludes the use of expensive sensors with negligible noise effects.
Previous techniques typically fail to address situations where the intensity constancy assumption is not satisfied. Some previous techniques use variations of the intensity constancy assumption that account for gradual changes in image intensity over time, corresponding to changes in surface shading. Other previous techniques regularize image data by convolving it with a smoothing function such as a Gaussian before estimating spatio-temporal derivatives that are required for computing optical flow. Such a smoothing function somewhat attenuates the effects of sensor noise, but noise reduction is ad hoc, and computed optical flow fields still tend to be noise sensitive.
It would be advantageous to reduce the effect of sensor noise on image data prior to optical flow computation, thereby decreasing the sensitivity of optical flow fields to sensor noise and hence improving their utility in realistic situations. Such a technique would be particularly suited to motion estimation and moving target indication from a stationary sensor, or one that may have electronic or mechanical drift.
Thus, a need has arisen for a method and system for indicating a change between images, in which accuracy of optical flow computations is less sensitive to noise in image data. Also, a need has arisen for a method and system for indicating a change between images, in which target movement between successive images is accurately determined. Further, a need has arisen for a method and system for indicating a change between images, in which effects of sensor noise on image intensity values are reduced prior to optical flow computations. Moreover, a need has arisen for a method and system for indicating a change between images, in which increased accuracy of optical flow computations is not ad hoc.